d4262bca1e7f48775cd45562ff7e8a77dae739b1 gperez2 Wed Apr 24 11:41:11 2024 -0700 Combining the two abSplice.ra and html files for hg38 and hg19, refs #33251 diff --git src/hg/makeDb/trackDb/human/hg19/abSplice.html src/hg/makeDb/trackDb/human/hg19/abSplice.html deleted file mode 100644 index 3bf5db4..0000000 --- src/hg/makeDb/trackDb/human/hg19/abSplice.html +++ /dev/null @@ -1,76 +0,0 @@ -
-
--AbSplice is a method that predicts aberrant splicing across human tissues, as described in Wagner, -Çelik et al., 2023. This track displays precomputed AbSplice scores for all possible single-nucleotide -variants genome-wide. The scores represent the probability that a given variant causes aberrant -splicing in a given tissue. - AbSplice scores -can be computed from VCF files and are based on quantitative tissue-specific splice site annotations -(SpliceMaps). -While SpliceMaps can be generated for any tissue of interest from a cohort of RNA-seq samples, this -track includes 49 tissues available from the -Genotype-Tissue Expression (GTEx) dataset. -
- --The AbSplice score is a probability estimate of how likely aberrant splicing of some sort takes -place in a given tissue. -The authors suggest -three cutoffs which are represented by color in the track. - -
-Mouseover on items shows the gene name, maximum score, and tissues that had this score. Clicking on -any item brings up a table with scores for all 48 GTEX tissues. -
- -AbSplice scores are also available at the -public repository -created by the authors. - -
-Data was converted from the file -(AbSplice_DNA_hg19_snvs_high_scores.zip) provided by the authors at -zenodo.org. Files in the -score_cutoff=0.01 directory were concatenated. To convert the data to bigBed format, scores and -their tissues were selected from the AbSplice_DNA fields and maximum scores, and then calculated using a - -custom Python script. -
- -Thanks to Nils Wagner for helpful comments and suggestions.
- --Wagner N, Çelik MH, Hölzlwimmer FR, Mertes C, Prokisch H, Yépez VA, Gagneur J. - -Aberrant splicing prediction across human tissues. -Nat Genet. 2023 May;55(5):861-870. -PMID: 37142848 -